Objectives: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on admission rates and in-hospital mortality among patients with ischemic and hemorrhagic stroke. Methods: We constructed a dataset detailing the monthly hospitalizations and mortality rates of inpatients with stroke from January 2017 to December 2021. Employing an interrupted time series analysis, we explored the impact of COVID-19 on hospitalizations and 30-day in-hospital mortality among stroke patients. Results: The number of ischemic stroke admissions decreased by 18.5%, from 5335 to 4348, immediately following the COVID-19 outbreak (p<0.001). The in-hospital mortality rate for ischemic stroke increased slightly from 3.3% to 3.4% immediately after the outbreak, although it showed a decreasing trend over time. The number of hemorrhagic stroke admissions fell by 7.5%, from 2014 to 1864, immediately following the COVID-19 outbreak. The 30-day in-hospital mortality rate for hemorrhagic stroke initially decreased from 12.9% to 12.7%, but subsequently showed an increasing trend. Conclusions We confirmed that COVID-19 impacted both the admission and death rates of stroke patients. The admission rate for both ischemic and hemorrhagic strokes decreased, while in-hospital mortality increased. Specifically, in-hospital mortality from ischemic stroke rose initially after the outbreak before stabilizing. Additionally, our findings indicate variable effects based on sex, age, and socioeconomic status, suggesting that certain groups may be more susceptible. This underscores the need to identify and support vulnerable populations to mitigate adverse health outcomes.

Objectives: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on admission rates and in-hospital mortality among patients with ischemic and hemorrhagic stroke. Methods: We constructed a dataset detailing the monthly hospitalizations and mortality rates of inpatients with stroke from January 2017 to December 2021. Employing an interrupted time series analysis, we explored the impact of COVID-19 on hospitalizations and 30-day in-hospital mortality among stroke patients. Results: The number of ischemic stroke admissions decreased by 18.5%, from 5335 to 4348, immediately following the COVID-19 outbreak (p<0.001). The in-hospital mortality rate for ischemic stroke increased slightly from 3.3% to 3.4% immediately after the outbreak, although it showed a decreasing trend over time. The number of hemorrhagic stroke admissions fell by 7.5%, from 2014 to 1864, immediately following the COVID-19 outbreak. The 30-day in-hospital mortality rate for hemorrhagic stroke initially decreased from 12.9% to 12.7%, but subsequently showed an increasing trend. Conclusions We confirmed that COVID-19 impacted both the admission and death rates of stroke patients. The admission rate for both ischemic and hemorrhagic strokes decreased, while in-hospital mortality increased. Specifically, in-hospital mortality from ischemic stroke rose initially after the outbreak before stabilizing. Additionally, our findings indicate variable effects based on sex, age, and socioeconomic status, suggesting that certain groups may be more susceptible. This underscores the need to identify and support vulnerable populations to mitigate adverse health outcomes.

Objectives: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on admission rates and in-hospital mortality among patients with ischemic and hemorrhagic stroke. Methods: We constructed a dataset detailing the monthly hospitalizations and mortality rates of inpatients with stroke from January 2017 to December 2021. Employing an interrupted time series analysis, we explored the impact of COVID-19 on hospitalizations and 30-day in-hospital mortality among stroke patients. Results: The number of ischemic stroke admissions decreased by 18.5%, from 5335 to 4348, immediately following the COVID-19 outbreak (p<0.001). The in-hospital mortality rate for ischemic stroke increased slightly from 3.3% to 3.4% immediately after the outbreak, although it showed a decreasing trend over time. The number of hemorrhagic stroke admissions fell by 7.5%, from 2014 to 1864, immediately following the COVID-19 outbreak. The 30-day in-hospital mortality rate for hemorrhagic stroke initially decreased from 12.9% to 12.7%, but subsequently showed an increasing trend. Conclusions We confirmed that COVID-19 impacted both the admission and death rates of stroke patients. The admission rate for both ischemic and hemorrhagic strokes decreased, while in-hospital mortality increased. Specifically, in-hospital mortality from ischemic stroke rose initially after the outbreak before stabilizing. Additionally, our findings indicate variable effects based on sex, age, and socioeconomic status, suggesting that certain groups may be more susceptible. This underscores the need to identify and support vulnerable populations to mitigate adverse health outcomes.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Purpose: The purpose of this study is to determine the level of health equity in relation to cancer incidence. Materials and Methods: We used the National Health Insurance claims data of the National Health Insurance Service between 2005 and 2022 and annual health insurance and medical aid beneficiaries between 2011 and 2021 to investigate the disparities of cancer incidence. We calculated age-sex standardized cancer incidence rates by cancer and year according to the type of insurance and the trend over time using the annual percentage change. We also compared the hospital type of the first diagnosis by cancer type and year and cancer incidence rates by cancer type and region in 2021 according to the type of insurance. Results: The total cancer incidence increased from 255,971 in 2011 to 325,772 cases in 2021. The absolute difference of total cancer incidence rate between the NHI beneficiaries and the medical aid (MA) recipients increased from 510.1 cases per 100,000 population to 536.9 cases per 100,000 population. The odds ratio of total cancer incidence for the MA recipients increased from 1.79 (95% confidence interval [CI], 1.77 to 1.82) to 1.90 (95% CI, 1.88 to 1.93). Disparities in access to hospitals and regional cancer incidence were profound. Conclusion This study examined health inequities in relation to cancer incidence over the last decade. Cancer incidence was higher in the MA recipients, and the gap was widening. We also found that regional differences in cancer incidence still exist and are getting worse. Investigating these disparities between the NHI beneficiaries and the MA recipients is crucial for implementing of public health policies to reduce health inequities.

OBJECTIVES: In Korea, the National Health Insurance Service (NHIS) covers essential healthcare expenses, including cataract surgery. To address concerns that private health insurance (PHI) might have inflated the need for such procedures, we investigated the extent of the PHI-attributable increase in cataract surgery and its impact on NHIS-reimbursed expenses. METHODS: This retrospective, observational study uses nationwide claims data for cataract surgery from 2016 to 2020. We examined trends in utilization and cost, and we estimated the excess numbers of (1) cataract operations attributable to PHI and (2) types of intraocular lenses used for cataract surgery in 2020. RESULTS: Between 2016 and 2020, a 36.8% increase occurred in the number of cataract operations, with increases of 63.5% and 731.8% in the total healthcare costs reimbursed by NHIS and PHI, respectively. Over a 5-year period, the surgical rate per 100,000 people doubled for patients aged <65 years (from 328 in 2016 to 664 in 2020). Among the 619,771 cases in 2020 of cataract surgery reimbursed by the Korean diagnosis-related group system, more non-NHIS-covered intraocular lenses were used for patients aged <65 years than ≥65 years (68.1 vs. 14.2%). In 2020 alone, an estimated 129,311 excess operations occurred, accounting for an excess cost of US$115 million. CONCLUSIONS A dramatic increase in the number and cost of cataract operations has occurred over the last 5 years. The PHI-related increase in operations resulted in increased costs to NHIS. Measures to curtail the non-indicated use of cataract surgery should be implemented regarding PHI.

Background: The epidemiology of influenza is commonly used to understand and establish relevant health policies for emerging respiratory infections, including coronavirus disease 2019 (COVID-19). However, Korea has no confirmed nationwide data on influenza incidence, severity, and mortality rate. Methods: We conducted a cross-sectional study to obtain epidemic data on influenza at the national level using National Health Insurance claims data during 2010 to 2020. Influenza cases were defined as 90-day timeframe episodes based on all inpatient and outpatient claims data with disease code J09, J10, and J11. Influenza incidence, severity, and mortality rate were calculated, and logistic regressions were performed to assess the associations of demographic characteristics and comorbidity with influenza-related hospitalization, severe illness, and death. Results: There were 0.3–5.9% influenza cases in the population from 2010 to 2020, with 9.7–18.9%, 0.2–0.9%, and 0.03–0.08% hospitalized, used in the intensive care unit, and dead, respectively. Age-standardized incidence and mortality rates were 424.3–6847.4 and 0.2–1.9 per 100,000 population, respectively. While more than half of the influenza cases occurred in populations aged younger than 20 years, deaths in older than 60 years accounted for more than two-thirds of all deaths. Conclusion This study provided the simplest but most important statistics regarding Korean influenza epidemics as a reference. These can be used to understand and manage other new acute respiratory diseases, including COVID-19, and establish influenza-related policies.